Python numpy.__name__() Examples

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def prepare_filter(args, dataset):
    iekf = TORCHIEKF()

    # set dataset parameter
    iekf.filter_parameters = args.parameter_class()
    iekf.set_param_attr()
    if type(iekf.g).__module__ == np.__name__:
        iekf.g = torch.from_numpy(iekf.g).double()

    # load model
    if args.continue_training:
        iekf.load(args, dataset)
    iekf.train()
    # init u_loc and u_std
    iekf.get_normalize_u(dataset)
    return iekf 

def _model_to_dict(model):
        """Convert a sklearn model object to a dictionary"""
        dictionary = {
            "module": type(model).__module__,
            "class": type(model).__name__,
            "params": model.get_params(deep=True),
            "coefs": {
                attr: copy.deepcopy(getattr(model, attr))
                for attr in model.__dir__()
                if not attr.startswith("__") and attr.endswith("_")
            }
        }

        if "tree_" in dictionary["coefs"]:
            # Not funny. sklearn.tree objects are not directly
            # serializable to json. Hence, we must dump them by ourselves.
            dictionary["coefs"]["tree_"] = RetrievalProduct._tree_to_dict(
                dictionary["coefs"]["tree_"]
            )

        return RetrievalProduct._encode_numpy(dictionary) 

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape 

def _updated_config(self):
    """Util hared between different serialization methods.

    Returns:
        Model config with Keras version information added.
    """
    from tensorflow.contrib.keras.python.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top

    config = self.get_config()
    model_config = {
        'class_name': self.__class__.__name__,
        'config': config,
        'keras_version': keras_version,
        'backend': K.backend()
    }
    return model_config 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def all_same(val_array):
    '''
    Check if all the values in given list the same

    Parameters
    ----------
    numpy_list: numpy array
    '''

    # check if val_array is a numpy array
    if type(val_array).__module__ == np.__name__:
        val = val_array.tolist()
        if isinstance(val[0], list):
            #handle list of lists
            return all(round(x[0]) == round(val[0][0]) for x in val)
        else:
            #handle single list
            return all(round(x) == round(val[0]) for x in val)
    else:
        raise Exception('This function only takes a numpy array') 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape 

def face_covered_frame(self, input_frame_with_faces):
        """Function to draw black recs over detected faces.

        This function remove eny 'noise' and help detector detecting palm.
        :param input_frame_with_faces (np.ndarray): a frame with faces, that needed to be covered.
        """

        try:
            # make sure input is np.ndarray
            assert type(input_frame_with_faces).__module__ == np.__name__
        except AssertionError as error:
            self.logger.exception(error)
            return

        # Preparation
        self._preprocessed_input_frame = input_frame_with_faces.copy()
        gray = cv2.cvtColor(self._preprocessed_input_frame, cv2.COLOR_BGR2GRAY)
        faces = self._face_detector.detectMultiScale(gray, 1.3, 5)

        # Black rectangle over faces to remove skin noises.
        for (x, y, w, h) in faces:
            self._preprocessed_input_frame[y - self._face_padding_y:y + h + self._face_padding_y,
            x - self._face_padding_x:x + w + self._face_padding_x, :] = 0 

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def type_to_builtin_type(type):
    # Infer from numpy type if it is one
    if type.__module__ == np.__name__:
        return numpy_type_to_builtin_type(type)

    # Otherwise, try to infer from a few generic python types
    if np.issubclass_(type, bool):
        return types_bool
    elif np.issubclass_(type, six.integer_types):
        return types_int32
    elif np.issubclass_(type, six.string_types):
        return types_str
    elif np.issubclass_(type, float):
        return types_fp32
    else:
        raise TypeError("Could not determine builtin type for " + str(type)) 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape 

def count_params(self):
        """Counts the total number of scalars composing the weights.

        # Returns
            An integer count.

        # Raises
            RuntimeError: if the layer isn't yet built
                (in which case its weights aren't yet defined).
        """
        if not self.built:
            if self.__class__.__name__ == 'Sequential':
                self.build()
            else:
                raise RuntimeError('You tried to call `count_params` on ' +
                                   self.name + ', but the layer isn\'t built. '
                                   'You can build it manually via: `' +
                                   self.name + '.build(batch_input_shape)`.')
        return count_params(self.weights) 

def _updated_config(self):
        """Util hared between different serialization methods.

        # Returns
            Model config with Keras version information added.
        """
        from .. import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version,
            'backend': K.backend()
        }
        return model_config 

def compute_output_shape(self, input_shape):
        """Computes the output shape of the layer.

        Assumes that the layer will be built
        to match that input shape provided.

        # Arguments
            input_shape: Shape tuple (tuple of integers)
                or list of shape tuples (one per output tensor of the layer).
                Shape tuples can include None for free dimensions,
                instead of an integer.

        # Returns
            An input shape tuple.
        """
        if hasattr(self, 'get_output_shape_for'):
            msg = "Class `{}.{}` defines `get_output_shape_for` but does not override `compute_output_shape`. " + \
                  "If this is a Keras 1 layer, please implement `compute_output_shape` to support Keras 2."
            warnings.warn(msg.format(type(self).__module__, type(self).__name__), stacklevel=2)
        return input_shape